Single-cell RNA sequencing (scRNA-seq) allows researchers to collect large catalogues
detailing the transcriptomes of individual cells. Unsupervised clustering is of central …

Researchers view vast zeros in single-cell RNA-seq data differently: some regard zeros as
biological signals representing no or low gene expression, while others regard zeros as …

This protocol explains how to perform a fast SCENIC analysis alongside standard best
practices steps on single-cell RNA-sequencing data using software containers and Nextflow …

We present SCENIC, a computational method for simultaneous gene regulatory network
reconstruction and cell-state identification from single-cell RNA-seq data (http://scenic …

Kidney organoids derived from human pluripotent stem cells have great utility for
investigating organogenesis and disease mechanisms and, potentially, as a replacement …

Motivation Single-cell RNA sequencing (scRNA-seq) is increasingly used to study gene
expression at the level of individual cells. However, preparing raw sequence data for further …

An ongoing technological revolution is continually improving our ability to carry out very high-
resolution studies of gene expression patterns. Current technology enables the global gene …

Single-cell RNA-seq enables the quantitative characterization of cell types based on global
transcriptome profiles. We present single-cell consensus clustering (SC3), a user-friendly …

Cardiac fibroblasts convert to myofibroblasts with injury to mediate healing after acute
myocardial infarction (MI) and to mediate long-standing fibrosis with chronic disease …

Background The analysis of single-cell RNA sequencing (scRNAseq) data plays an
important role in understanding the intrinsic and extrinsic cellular processes in biological …